{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T04:02:39Z","timestamp":1751515359670,"version":"3.41.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2018,3,1]],"date-time":"2018-03-01T00:00:00Z","timestamp":1519862400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61501230"],"award-info":[{"award-number":["61501230"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"crossref","award":["BK20150751"],"award-info":[{"award-number":["BK20150751"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control","award":["MJUKF201726"],"award-info":[{"award-number":["MJUKF201726"]}]},{"name":"China Post-doctoral Science Foundation funded project","award":["2015M570446"],"award-info":[{"award-number":["2015M570446"]}]},{"DOI":"10.13039\/501100010242","name":"Jiangsu Planned Projects for Postdoctoral Research Funds","doi-asserted-by":"crossref","award":["1402047B"],"award-info":[{"award-number":["1402047B"]}],"id":[{"id":"10.13039\/501100010242","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1007\/s13042-018-0802-0","type":"journal-article","created":{"date-parts":[[2018,3,1]],"date-time":"2018-03-01T07:53:34Z","timestamp":1519890814000},"page":"1581-1589","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An alternative to face image representation and classification"],"prefix":"10.1007","volume":"10","author":[{"given":"Qi","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donghai","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nuoya","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huijie","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,3,1]]},"reference":[{"key":"802_CR1","doi-asserted-by":"publisher","first-page":"10311044","DOI":"10.1109\/JPROC.2010.2044470","volume":"98","author":"J Wright","year":"2010","unstructured":"Wright J, Ma Y, Mairal J, Sapiro G, Huang TS, Yan SC (2010) Sparse representation for computer vision and pattern recognition. Proc IEEE 98:1031\u20131044","journal-title":"Proc IEEE"},{"key":"802_CR2","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1109\/ACCESS.2015.2430359","volume":"3","author":"Z Zhang","year":"2015","unstructured":"Zhang Z, Xu Y, Yang J, Li X, Zhang D (2015) A survey of sparse representation: algorithms and applications. IEEE Access 3:490\u2013530","journal-title":"IEEE Access"},{"key":"802_CR3","first-page":"18","volume":"52","author":"K Kroeker","year":"2009","unstructured":"Kroeker K (2009) Face recognition breakthrough. Commun ACM 52:18\u201319","journal-title":"Commun ACM"},{"key":"802_CR4","unstructured":"Qiu Q, Jiang Z, Chellappa R (2011) Sparse dictionary-based representation and recognition of action attributes. In: Proceedings of the international conference on computer vision (ICCV), pp\u00a0707\u2013714"},{"key":"802_CR5","doi-asserted-by":"crossref","unstructured":"Dong W, Li X, Zhang L, Shi G (2011) Sparsity-based image denoising via dictionary learning and structural clustering. In: IEEE conference on computer vision pattern recognition, pp 457\u2013464","DOI":"10.1109\/CVPR.2011.5995478"},{"key":"802_CR6","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","volume":"19","author":"J Yang","year":"2010","unstructured":"Yang J, Wright J, Huang T, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19:2861\u20132873","journal-title":"IEEE Trans Image Process"},{"key":"802_CR7","unstructured":"Yang J, Wright J, Huang T, Ma Y (2008) Image super-resolution as sparse representation of raw image patches. In: IEEE conference on computer vision and pattern recognition, pp\u00a01\u20138"},{"key":"802_CR8","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1109\/TIP.2005.863057","volume":"15","author":"O Guleryuz","year":"2006","unstructured":"Guleryuz O (2006) Nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising-part I: theory. IEEE Trans Image Process 15:539\u2013554","journal-title":"IEEE Trans Image Process"},{"key":"802_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2772264","author":"Z Zhang","year":"2017","unstructured":"Zhang Z, Shao L, Xu Y, Liu L, Yang J (2017) Marginal representation learning with graph structure self-adaptation. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2017.2772264","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"802_CR10","doi-asserted-by":"crossref","unstructured":"Mei X, Ling H, Jacobs D, Illumination recovery from image with cast shadows via sparse representation. IEEE Trans Image Process 20(2011):2366\u20132377","DOI":"10.1109\/TIP.2011.2118222"},{"key":"802_CR11","first-page":"21972202","volume":"100","author":"D Donoho","year":"2003","unstructured":"Donoho D, Elad M (2003) Optimally sparse representation in general (nonorthogonal) dictionaries via \u21131 minimization. Proc Natl Acad Sci 100:2197\u20132202","journal-title":"Proc Natl Acad Sci"},{"key":"802_CR12","doi-asserted-by":"crossref","unstructured":"Pati Y, Ramin R, Krishnaprasad P (1993) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: IEEE conference record of the twenty-seventh asilomar conference on signals systems computers, pp 40\u201344","DOI":"10.1109\/ACSSC.1993.342465"},{"key":"802_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2016.2638083","author":"X Xue","year":"2016","unstructured":"Xue X, Han H, Wang S, Qin C (2016) Computational experiment-based evaluation on context-aware O2O service recommendation. IEEE Trans Serv Comput. https:\/\/doi.org\/10.1109\/TSC.2016.2638083","journal-title":"IEEE Trans Serv Comput"},{"key":"802_CR14","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1002\/cpa.20132","volume":"59","author":"D Donoho","year":"2006","unstructured":"Donoho D (2006) For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution. Commun Pure Appl Math 59:797\u2013829","journal-title":"Commun Pure Appl Math"},{"issue":"3","key":"802_CR15","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1109\/TIP.2017.2651396","volume":"26","author":"Z Zhang","year":"2017","unstructured":"Zhang Z, Lai Z, Xu Y, Shao L, Wu J, Xie G (2017) Discriminative elastic-net regularized linear regression. Accept IEEE Trans Image Process 26(3):1466\u20131481","journal-title":"Accept IEEE Trans Image Process"},{"key":"802_CR16","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1109\/TIP.2015.2503238","volume":"25","author":"Q Liu","year":"2016","unstructured":"Liu Q, Lai Z, Zhou Z, Kuang F, Jin Z (2016) A truncated nuclear norm regularization method based on weighted residual error for matrix completion. IEEE Trans Image Process 25:316\u2013330","journal-title":"IEEE Trans Image Process"},{"key":"802_CR17","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.patcog.2011.07.003","volume":"45","author":"I Naseem","year":"2012","unstructured":"Naseem I, Togneri R, Bennamoun M (2012) Robust regression for face recognition. Pattern Recogn 45:104\u2013118","journal-title":"Pattern Recogn"},{"key":"802_CR18","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.jvcir.2015.01.001","volume":"28","author":"Z Fan","year":"2015","unstructured":"Fan Z, Ni M, Zhu Q et al (2015) L0-norm sparse representation based on modified genetic algorithm for face recognition. J Vis Commun Image Represent 28:15\u201320","journal-title":"J Vis Commun Image Represent"},{"key":"802_CR19","doi-asserted-by":"crossref","unstructured":"Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition? In: Proceedings of the 2011 IEEE international conference on computer vision, pp 471\u2013478","DOI":"10.1109\/ICCV.2011.6126277"},{"key":"802_CR20","doi-asserted-by":"publisher","first-page":"2708","DOI":"10.1016\/j.patcog.2012.01.003","volume":"45","author":"C Ren","year":"2012","unstructured":"Ren C, Dai D, Yan H (2012) Robust classification using \u21132,1-norm based regression model. Pattern Recogn 45:2708\u20132718","journal-title":"Pattern Recogn"},{"key":"802_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS","author":"Z Zhang","year":"2017","unstructured":"Zhang Z, Xu Y, Shao L, Yang J (2017) Discriminative block-diagonal representational learning for image recognition. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"802_CR22","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1109\/TNNLS.2015.2422994","volume":"27","author":"Z Lai","year":"2016","unstructured":"Lai Z, Wong W, Xu Y, Yang J, Tang J, Zhang D (2016) Approximate orthogonal sparse embedding for dimensionality reduction. IEEE Trans Neural Netw Learn Syst 27:723\u2013735","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"802_CR23","first-page":"1","volume":"6314","author":"S Gao","year":"2010","unstructured":"Gao S, Tsang I, Chia L (2010) Kernel sparse representation for image classification and face recognition. Comput Vis ECCV 6314:1\u201314","journal-title":"Comput Vis ECCV"},{"key":"802_CR24","unstructured":"Yang S, Han Y, Zhang X (2012) A sparse kernel representation method for image classification. In: The 2012 international joint conference on neural networks (IJCNN), pp\u00a01\u20137"},{"key":"802_CR25","first-page":"1269","volume":"9","author":"A d\u2019Aspremont","year":"2008","unstructured":"d\u2019Aspremont A, Bach F, Ghaoui L (2008) Optimal solutions for sparse principal component analysis. J Mach Learn Res 9:1269\u20131294","journal-title":"J Mach Learn Res"},{"key":"802_CR26","doi-asserted-by":"publisher","first-page":"1942","DOI":"10.1109\/TNNLS.2013.2297381","volume":"25","author":"Z Lai","year":"2014","unstructured":"Lai Z, Xu Y, Chen Q, Yang J, Zhang D (2014) Multilinear sparse principal component analysis. IEEE Trans Neural Netw Learn Syst 25:1942\u20131950","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"802_CR27","doi-asserted-by":"publisher","first-page":"1779","DOI":"10.1109\/TNNLS.2013.2295717","volume":"25","author":"Z Lai","year":"2014","unstructured":"Lai Z, Wong WK, Xu Y, Sun M, Zhao C (2014) Sparse alignment for robust tensor learning. IEEE Trans Neural Netw Learn Syst 25:1779\u20131792","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"802_CR28","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1137\/080716542","volume":"2","author":"B Amir","year":"2009","unstructured":"Amir B, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2:183\u2013202","journal-title":"SIAM J Imaging Sci"},{"key":"802_CR29","unstructured":"Asif MS, Romberg J (2013) Sparse recovery of streaming signals using l1-homotopy. arXiv:1306.3331"},{"key":"802_CR30","unstructured":"Yang A, Zhou Z, Ganesh A et al (2010) Fast \u21131-minimization algorithms for robust face recognition. arXiv:1007.3753"},{"key":"802_CR31","unstructured":"Yang A, Sastry S, Ganesh A et al (2010) Fast \u21131-minimization algorithms and an application in robust face recognition: a review. In: The 17th IEEE international conference on image processing (ICIP), pp 1849\u20131852"},{"key":"802_CR32","unstructured":"Zhang L, Yang M, Feng X et al (2012) Collaborative representation based classification for face recognition. arXiv:1204.2358"},{"key":"802_CR33","unstructured":"Yang M, Zhang L, Zhang D et al (2012) Relaxed collaborative representation for pattern classification. In: IEEE conference on computer vision and pattern recognition, pp\u00a02224\u20132231"},{"key":"802_CR34","unstructured":"Zhu Q, Xu Y, Wang J et al (2012) Kernel based sparse representation for face recognition. In: 21st International conference on pattern recognition, pp 1703\u20131706"},{"key":"802_CR35","doi-asserted-by":"crossref","unstructured":"Dornaika F, Traboulsi Y, Hernandez C, Assoum A (2013) Self-optimized two phase test sample sparse representation method for image classification. In: The 2nd international conference on advances in biomedical engineering (ICABME), pp 163\u2013166","DOI":"10.1109\/ICABME.2013.6648873"},{"key":"802_CR36","doi-asserted-by":"crossref","unstructured":"Dornaika F, Traboulsi Y, Assoum A (2013) Adaptive two phase sparse representation classifier for face recognition. In: Advanced concepts for intelligent vision systems, pp\u00a0182\u2013191","DOI":"10.1007\/978-3-319-02895-8_17"},{"issue":"1","key":"802_CR37","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1109\/TPAMI.2016.2535218","volume":"39","author":"J Yang","year":"2017","unstructured":"Yang J, Luo L, Qian J, Tai Y, Zhang F (2017) Nuclear norm based matrix regression with applications to face recognition with occlusion and illumination changes. IEEE Trans Pattern Anal Mach Intell 39(1):156\u2013171","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"802_CR38","doi-asserted-by":"publisher","first-page":"3145","DOI":"10.1016\/j.patcog.2015.04.017","volume":"48","author":"J Qian","year":"2015","unstructured":"Qian J, Luo L, Yang J, Zhang F, Lin Z (2015) Robust nuclear norm regularized regression for face recognition with occlusion. Pattern Recogn 48(10):3145\u20133159","journal-title":"Pattern Recogn"},{"key":"802_CR39","doi-asserted-by":"crossref","unstructured":"Qian J, Yang J (2013) General regression and representation model for face recognition. In: Biometrics workshop in conjunction with IEEE conference on computer vision and pattern recognition (CVPRW), pp\u00a0166\u2013172","DOI":"10.1109\/CVPRW.2013.32"},{"issue":"3","key":"802_CR40","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.3233\/JIFS-162297","volume":"33","author":"RAR Ashfaq","year":"2017","unstructured":"Ashfaq RAR, Wang XZ (2017) Impact of fuzziness categorization on divide and conquer strategy for instance selection. J Intell Fuzzy Syst 33(3):1007\u20131018","journal-title":"J Intell Fuzzy Syst"},{"key":"802_CR41","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.ins.2016.04.019","volume":"378","author":"RAR Ashfaq","year":"2017","unstructured":"Ashfaq RAR, Wang XZ, Huang JZ et al (2017) Fuzziness based semi-supervised learning approach for intrusion detection system. Inf Sci 378:484\u2013497","journal-title":"Inf Sci"},{"issue":"5","key":"802_CR42","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1109\/TCYB.2013.2263382","volume":"44","author":"XZ Wang","year":"2014","unstructured":"Wang XZ, Wang R, Feng HM, Wang HC (2014) A new approach to classifier fusion based on upper integral. IEEE Trans Cybern 44(5):620\u2013635","journal-title":"IEEE Trans Cybern"},{"key":"802_CR43","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/s10559-016-9807-4","volume":"52","author":"MS Lavreniuk","year":"2016","unstructured":"Lavreniuk MS, Skaku SV, Shelestov AJ et al (2016) Large-scale classification of land cover using retrospective satellite data. Cybern Syst Anal 52:127\u2013138","journal-title":"Cybern Syst Anal"},{"key":"802_CR44","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1109\/TITS.2015.2479925","volume":"17","author":"RO Chavez-Garcia","year":"2016","unstructured":"Chavez-Garcia RO, Aycard O (2016) Multiple sensor fusion and classification for moving object detection and tracking. IEEE Trans Intell Transp Syst 17:525\u2013534","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"12","key":"802_CR45","doi-asserted-by":"publisher","first-page":"6989","DOI":"10.1109\/TGRS.2017.2737780","volume":"55","author":"X Guan","year":"2017","unstructured":"Guan X, Liu G, Huang C et al (2017) An object-based linear weight assignment fusion scheme to improve classification accuracy using Landsat and MODIS data at the decision level. IEEE Trans Geosci Remote Sens\u00a055(12):6989\u20137002","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"7","key":"802_CR46","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1049\/iet-ipr.2016.0988","volume":"11","author":"H Liu","year":"2017","unstructured":"Liu H, Yan M, Song E et al (2017) Label fusion method based on sparse patch representation for the brain MRI image segmentation. IET Image Process\u00a011(7):502\u2013511","journal-title":"IET Image Process"},{"key":"802_CR47","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.patcog.2016.10.009","volume":"63","author":"T Tong","year":"2017","unstructured":"Tong T, Gray K, Gao Q et al (2017) Multi-modal classification of Alzheimer\u2019s disease using nonlinear graph fusion. Pattern Recogn 63:171\u2013181","journal-title":"Pattern Recogn"},{"key":"802_CR48","unstructured":"Nan Z, Yang J (2010) K Nearest Neighbor based local sparse representation classifiers. In: 2010 Chinese conference on pattern recognition (CCPR), pp\u00a01\u20135"},{"key":"802_CR49","doi-asserted-by":"crossref","unstructured":"Ma L, Xiao B, Wang C (2010) Sparse representation based on K-nearest neighbor classifier for degraded Chinese character recognitions. In: Advances in multimedia information processing\u2014PCM 2010, pp\u00a0506\u2013514","DOI":"10.1007\/978-3-642-15696-0_47"},{"key":"802_CR50","unstructured":"https:\/\/www.nist.gov\/programs-projects\/face-recognition-technology-feret . Accessed 15 Feb 2017"},{"key":"802_CR51","unstructured":"http:\/\/www.cl.cam.ac.uk\/research\/dtg\/attarchive\/facedatabase.html . Accessed 15 Feb 2017"},{"key":"802_CR52","unstructured":"http:\/\/www.anefian.com\/research\/face_reco . Accessed 15 Feb 2017"},{"key":"802_CR53","unstructured":"http:\/\/vis-www.cs.umass.edu\/lfw . Accessed 15 Feb 2017"},{"key":"802_CR54","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1109\/JSTSP.2007.910971","volume":"1","author":"S Kim","year":"2007","unstructured":"Kim S, Koh K, Lustig M et al (2007) An interior-point method for large-scale \u21131-regularized least squares. IEEE J Select Top Signal Process 1:606\u2013617","journal-title":"IEEE J Select Top Signal Process"},{"key":"802_CR55","first-page":"407","volume":"39","author":"Z Liu","year":"2013","unstructured":"Liu Z, Pu J, Huang T, Qiu Y (2013) A novel classification method for palmprint recognition based on reconstruction error and normalized distance. Appl Intell 39:407\u2013414","journal-title":"Appl Intell"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-018-0802-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-018-0802-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-018-0802-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T04:54:27Z","timestamp":1751432067000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-018-0802-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,1]]},"references-count":55,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2019,7]]}},"alternative-id":["802"],"URL":"https:\/\/doi.org\/10.1007\/s13042-018-0802-0","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2018,3,1]]},"assertion":[{"value":"30 May 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}